The use of drones for mosquito surveillance and control

Horizons of the Future: Preparedness and Response

In the last decades, we have witnessed the worldwide spread and growing impact of one of the most important neglected tropical diseases, the dengue fever. Even though it continues to be neglected mainly due to its major impact on the more socio and economically vulnerable populations there was progress toward a more complete understanding about the basic biology of dengue infection in mosquitoes and humans as well as translational research to develop antivirals and improved vaccines. Paradoxically, dengue fever incidence has steadily grown globally suggesting that the development/refinement of basic/translational research and control approaches are not keeping the pace of dengue spread. Therefore, in this last chapter, we will discuss the latest developments regarding preparedness and response against dengue, looking into their applicability to reduce dengue fever around the globe.

Barriers and opportunities for community engagement in UAV-based dengue management in rural Malaysia

Dengue fever remains a significant public health issue in Malaysia, particularly in rural areas where unique challenges such as dispersed populations, limited infrastructure, and distinct socio-cultural dynamics complicate vector control efforts. Drone technology has emerged as an innovative tool for dengue management, offering capabilities such as aerial surveillance and targeted interventions. However, its adoption in rural communities is hindered by barriers related to community engagement and acceptance. This study aims to evaluate the barriers and opportunities for community engagement in drone-based dengue management within rural Malaysian settings. A cross-sectional study was conducted across six states representing rural Malaysia: Kelantan, Terengganu, Pahang, Johor, Kedah, and Perlis. A total of 190 participants were recruited using a stratified purposive sampling method. Data were collected via structured questionnaires assessing sociodemographic characteristics, perceptions of drone technology, and willingness to engage in dengue prevention activities, such as downloading a dengue-related application or participating in mosquito control training programs. Descriptive statistics and multinomial logistic regression were used to analyze factors influencing community engagement. Participants were predominantly female (67.4%) and of Malay ethnicity (>90%). Younger participants (<40 years) showed significantly lower willingness to participate in training programs ("Maybe" vs. "No": OR = 0.255, 95% CI: 0.067-0.968, p = 0.045), while age was not a significant predictor for app adoption. Negative perceptions of drone use and sociodemographic factors, such as housing type and residency duration, did not significantly influence willingness to engage. Despite these findings, qualitative responses highlighted concerns related to privacy, trust, and technological accessibility in rural areas. Drone-based dengue management in rural Malaysia faces challenges in community engagement, particularly among younger demographics. Tailored strategies, such as gamified training programs and targeted awareness campaigns, are necessary to address barriers and foster acceptance. These findings provide critical insights for developing inclusive and effective public health interventions leveraging drone technology in resource-limited rural settings.

Monitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control

BACKGROUND

Progress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. There is, therefore, increasing interest in the use of larval source management (LSM) to supplement current insecticide-based interventions. However, LSM implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. Remotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations.

METHODS

A method using radar imagery is proposed to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programmes aiming to implement LSM at scale. This is demonstrated for a 3971 km2 malaria-endemic district in Madagascar with over 17,000 rice fields. Rice field mapping on OpenStreetMap was combined with Sentinel-1 satellite imagery (radar, 10 m) from 2016 to 2022 to train a classification model of radar backscatter to identify rice fields with vegetated and open water, resulting in a time-series of weekly flooding dynamics for thousands of rice fields.

RESULTS

From these time-series, over a dozen indicators useful for LSM implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of LSM intervention (e.g. prioritization of sites, implementation, follow-up).

CONCLUSIONS

Scale-up of these methods could enable wider implementation of evidence-based LSM interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.

Factors affecting community participation in drone-based larviciding using Bacillus thuringiensis var. israelensis (Bti) for bio-control of malaria vectors in Rwanda

BACKGROUND

Malaria remains a significant health issue in Rwanda. Primary malaria prevention methods include insecticide-treated nets and indoor residual spraying as core interventions. Mosquito repellents, larval source management (LSM), and housing improvement are recommended as supplemental vector control methods. A 2020-2021 study in rice field habitats of peri-urban of Kigali City successfully evaluated the entomological and epidemiological impacts of drone-based larviciding using Bacillus thuringiensis var. israelensis (Bti).

METHODS

The present study employed a concurrent mixed-methods design to assess community knowledge, perception, acceptance, and willingness to participate in drone-based larviciding for malaria control in Kigali City. A total of 248 respondents participated in the quantitative survey interviews while five focus group discussions (FGDs), each comprising 10-12 participants, were conducted. Quantitative data were analysed using SPSS and R software, with logistic regression applied to identify factors influencing community participation. Qualitative data were manually coded and analysed thematically to complement the quantitative findings.

RESULTS

Participants showed widespread knowledge of malaria transmission and prevention, with high awareness of the importance of larviciding. A strong support of 96.4% expressed willingness to accept drone-based larviciding, including financial and free labour support. Factors influencing willingness to participate include occupation in rice and vegetable farming and mining (95% CI - 3.053 to - 0.169, p = 0.029), mosquito exposure (95% CI - 5.706 to - 1.293, p = 0.004). Participants highlighted drone-based larviciding role in reducing mosquitoes and malaria risk and recommended it's scaling up as a core component of integrated vector management (IVM).

CONCLUSIONS

This study highlights strong community awareness and acceptance of drone-based larviciding, with its effectiveness in reducing mosquito abundance and malaria risks, along with the safety of Bti and drones. The findings advocate integrating drone-based larviciding into national malaria control strategies by enhancing community education, building local expertise, and adopting innovative financing mechanisms for scalability and sustainability.

Elevating larval source management as a key strategy for controlling malaria and other vector-borne diseases in Africa

Larval source management (LSM) has a long history of advocacy and successes but is rarely adopted where funds are limited. The World Health Organization (WHO) guidelines on malaria prevention recommend the use of LSM as a supplementary intervention to the core vector control methods (insecticide-treated nets and indoor residual spraying), arguing that its feasibility in many settings can be limited by larval habitats being numerous, transient, and difficult to find or treat. Another key argument is that there is insufficient high-quality evidence for its effectiveness to support wide-scale implementation. However, the stagnation of progress towards malaria elimination demands that we consider additional options to the current emphasis on insecticidal commodities targeting adult mosquitoes inside homes. This letter is the result of a global, crossdisciplinary collaboration comprising: (a) detailed online expert discussions, (b) a narrative review of countries that have eliminated local malaria transmission, and (c) a mathematical modeling exercise using two different approaches. Together, these efforts culminated in seven key recommendations for elevating larval source management as a strategy for controlling malaria and other mosquito-borne diseases in Africa (Box 1). LSM encompasses the use of larvicide (a commodity) as well as various environmental sanitation measures. Together, these efforts lead to the long-term reduction of mosquito populations, which benefits the entire community by controlling both disease vector and nuisance mosquitoes. In this paper, we argue that the heavy reliance on large-scale cluster-randomized controlled trials (CRTs) to generate evidence on epidemiological endpoints restricts the recommendation of approaches to only those interventions that can be measured by functional units and deliver relatively uniform impact and, therefore, are more likely to receive financial support for conducting these trials. The explicit impacts of LSM may be better captured by using alternative evaluation approaches, especially high-quality operational data and a recognition of locally distinct outcomes and tailored strategies. LSM contributions are also evidenced by the widespread use of LSM strategies in nearly all countries that have successfully achieved malaria elimination. Two modelling approaches demonstrate that a multifaceted strategy, which incorporates LSM as a central intervention alongside other vector control methods, can effectively mitigate key biological threats such as insecticide resistance and outdoor biting, leading to substantial reductions in malaria cases in representative African settings. This argument is extended to show that the available evidence is sufficient to establish the link between LSM approaches and reduced disease transmission of mosquito-borne illnesses. What is needed now is a significant boost in the financial resources and public health administration structures necessary to train, employ and deploy local-level workforces tasked with suppressing mosquito populations in scientifically driven and ecologically sensitive ways. In conclusion, having WHO guidelines that recognize LSM as a key intervention to be delivered in multiple contextualized forms would open the door to increased flexibility for funding and aid countries in implementing the strategies that they deem appropriate. Financially supporting the scale-up of LSM with high-quality operations monitoring for vector control in combination with other core tools can facilitate better health. The global health community should reconsider how evidence and funding are used to support LSM initiatives.

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

Managing vector-borne diseases in a geoAI-enabled society. Malaria as an example

More than 17 % of all infectious diseases are caused by vector-borne diseases resulting in more than 1 billion cases and over 1 million deaths each year. Of these malaria continues to be a global burden in over eighty countries. As societies become more digitalised, the availability of geospatially enabled health and disease information will become more abundant. With this, the ability to assess health and disease risks in real-time will become a reality. The purpose of this study was to examine how geographic information, geospatial technologies and spatial data science are being used to reduce the burden of vector-borne diseases such as malaria and explore the opportunities that lie ahead with GeoAI and other geospatial technology advancements. Malaria is a dynamic and complex system and as such a range of data and approaches are needed to tackle different parts of the malaria cycle at different local and global scales. Geospatial technologies provide an integrated framework vital for monitoring, analysing and managing vector-borne diseases. GeoAI and technological advancements are useful for enhancing real-time assessments, accelerating the decision making process and spatial targeting of interventions. Training is needed to enhance the use of geospatial information for the management of vector-borne diseases.

A systematic review of Aedes aegypti population dynamics models based on differential equations

The global spread of Aedes aegypti and the associated public health risk have stimulated the development of several mathematical models to predict population dynamics in response to biological or environmental changes in real, future, or simulated scenarios. The aim of this study is to identify published articles on differential equation-based population dynamics models of Aedes aegypti, highlight their differences and commonalities, and examine their application in surveillance and control programs. Following the PRISMA guidelines, a systematic review was conducted in seven electronic databases (Scopus, PUBMED, IEEE Xplore, Science Direct, DOAJ, Scielo, and Google Scholar), with the last update on 8 February 2023. The initial search yielded 513 studies, of which 31 were finally selected. The articles analyzed showed great variability in the equations, processes, and variables included, with temperature being the most common environmental factor. Only a few models incorporated spatial heterogeneity or validation methods. Our findings suggest that improving the generation of temporal and spatially explicit forecasts through interdisciplinary collaboration, the use of new technologies, and validation with field data is essential for these models to effectively support public health efforts. Differential equation-based population dynamics models offer valuable insights and could greatly benefit mosquito surveillance programs if standardized and tailored to relevant scales.

Field surveys in rural Tanzania reveal key opportunities for targeted larval source management and species sanitation to control malaria in areas dominated by Anopheles funestus

BACKGROUND

Larval source management (LSM) is re-emerging as a critical malaria intervention to address challenges associated with core vector control tools, such as insecticide-treated nets (ITNs), and to accelerate progress towards elimination. Presently, LSM is not widely used in rural settings and is instead more commonly applied in urban and arid settings. A systematic entomological assessment was conducted in rural communities of southeastern Tanzania, where insecticide-treated nets (ITNs) are widely used, to explore opportunities for deploying LSM to improve malaria control.

METHODS

Aquatic habitat surveys were conducted in 2022 and 2023 to understand habitat usage by different mosquito vectors, covering five villages during the rainy season and seven villages during the dry season. Additionally, samples of adult mosquitoes were collected to assess the role of various Anopheles species in malaria transmission in the area, and to explore opportunities for species sanitation using targeted LSM.

RESULTS

Adult mosquito surveys showed that in this area, the total entomological inoculation rates (EIR) for indoor collections were 20.1 and 6.5 infectious bites per person per year for outdoors. Anopheles funestus and Anopheles arabiensis were the only Anopheles vectors identified. Anopheles funestus was responsible for over 97.6% of the malaria transmission indoors and 95.4% outdoors. The concurrent larval surveys found that habitats with late instar An. arabiensis and An. funestus comprised only a small subset of 11.2%-16.5% of all water bodies in the rainy season, and 9.7%-15.2% in the dry season. In terms of size, these habitats covered 66.4%-68.2% of the total habitat areas in the wet season, reducing to 33.9%-40.6% in the dry season. From the rainy season to the dry season, the surface area of habitats occupied by An. arabiensis and An. funestus decreased by 92.0% to 97.5%, while the number of habitats occupied by An. arabiensis and An. funestus decreased by 38.0% to 57.3%. Anopheles funestus preferred large, permanent habitats with clear water and vegetation year-round, while An. arabiensis showed contrasting seasonal preferences, favouring sunlit still waters in the rainy season and larger, opaque habitats in the dry season.

CONCLUSION

These findings suggest that An. funestus, which is the dominant malaria vector in the area, mediating over 95% of malaria transmission, preferentially occupies only a small subset of uniquely identifiable aquatic habitats in both wet and dry seasons. This presents an opportunity to expand LSM in rural settings by carefully targeting An. funestus habitats, which might be effective and logistically feasible as a complementary approach alongside existing interventions. Further research should assess the impact of targeted LSM for species sanitation compared to blanket LSM.

Smart technology for mosquito control: Recent developments, challenges, and future prospects

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Dengue is a product of the environment: an approach to the impacts of the environment on the Aedes aegypti mosquito and disease cases

Dengue is an arbovirus infection whose etiologic agent is transmitted by the Aedes aegypti mosquito. Since the early 1980s, when the circulation of the dengue virus (DENV) was confirmed in Brazil, the disease has become a growing multifactorial public health problem. This article presented the main factors that have contributed to the frequent dengue epidemics in recent years, such as the behavior of the vector, climate change, and social, political, and economic aspects. The intersection between these different factors in the dynamics of the disease is highlighted, including the increase in the mosquito population due to higher temperatures and rainy periods, as well as the influence of socioeconomic conditions on the incidence of dengue. Some mosquito control strategies are also addressed, including the use of innovative technologies such as drones and the Wolbachia bacterium, as well as the hope represented by the dengue vaccine. Nevertheless, the need for integrated and effective public policies to reduce social inequalities and the impacts of climate change on the spread of dengue is emphasized.

Trends and insights in dengue virus research globally: a bibliometric analysis (1995-2023)

BACKGROUND

Dengue virus (DENV) is the most widespread arbovirus. The World Health Organization (WHO) declared dengue one of the top 10 global health threats in 2019. However, it has been underrepresented in bibliometric analyses. This study employs bibliometric analysis to identify research hotspots and trends, offering a comprehensive overview of the current research dynamics in this field.

RESULTS

We present a report spanning from 1995 to 2023 that provides a unique longitudinal analysis of Dengue virus (DENV) research, revealing significant trends and shifts not extensively covered in previous literature. A total of 10,767 DENV-related documents were considered, with a notable increase in publications, peaking at 747 articles in 2021. Plos Neglected Tropical Diseases has become the leading journal in Dengue virus research, publishing 791 articles in this field-the highest number recorded. Our bibliometric analysis provides a comprehensive mapping of DENV research across multiple dimensions, including vector ecology, virology, and emerging therapies. The study delineates a complex network of immune response genes, including IFNA1, DDX58, IFNB1, STAT1, IRF3, and NFKB1, highlighting significant trends and emerging themes, particularly the impacts of climate change and new outbreaks on disease transmission. Our findings detail the progress and current status of key vaccine candidates, including the licensed Dengvaxia, newer vaccines such as Qdenga and TV003, and updated clinical trials. The study underscores significant advancements in antiviral therapies and vector control strategies for dengue, highlighting innovative drug candidates such as AT-752 and JNJ-1802, and the potential of drug repurposing with agents like Ribavirin, Remdesivir, and Lopinavir. Additionally, it discusses biological control methods, including the introduction of Wolbachia-infected mosquitoes and gene-editing technologies.

CONCLUSION

This bibliometric study underscores the critical role of interdisciplinary collaboration in advancing DENV research, identifying key trends and areas needing further exploration, including host-virus dynamics, the development and application of antiviral drugs and vaccines, and the use of artificial intelligence. It advocates for strengthened partnerships across various disciplines to effectively tackle the challenges posed by DENV.

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus

Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus, including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically.

Assessment of environmental and spatial factors influencing the establishment of Anopheles gambiae larval habitats in the malaria endemic province of Woleu-Ntem, northern Gabon

BACKGROUND

This study aimed to assess the spatial distribution of Anopheles mosquito larval habitats and the environmental factors associated with them, as a prerequisite for the implementation of larviciding.

METHODS

The study was conducted in December 2021, during the transition period between the end of the short rainy season (September-November) and the short dry season (December-February). Physical, biological, and land cover data were integrated with entomological observations to collect Anopheles larvae in three major towns: Mitzic, Oyem, and Bitam, using the "dipping" method during the transition from rainy to dry season. The collected larvae were then reared in a field laboratory established for the study period. After the Anopheles mosquitoes had emerged, their species were identified using appropriate morphological taxonomic keys. To determine the influence of environmental factors on the breeding of Anopheles mosquitoes, multiple-factor analysis (MFA) and a binomial generalized linear model were used.

RESULTS

According to the study, only 33.1% out of the 284 larval habitats examined were found to be positive for Anopheles larvae, which were primarily identified as belonging to the Anopheles gambiae complex. The findings of the research suggested that the presence of An. gambiae complex larvae in larval habitats was associated with various significant factors such as higher urbanization, the size and type of the larval habitats (pools and puddles), co-occurrence with Culex and Aedes larvae, hot spots in ambient temperature, moderate rainfall, and land use patterns.

CONCLUSIONS

The results of this research mark the initiation of a focused vector control plan that aims to eradicate or lessen the larval habitats of An. gambiae mosquitoes in Gabon's Woleu Ntem province. This approach deals with the root causes of malaria transmission through larvae and is consistent with the World Health Organization's (WHO) worldwide objective to decrease malaria prevalence in regions where it is endemic.

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

BACKGROUND

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.

RESULTS

We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.

CONCLUSIONS

Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Updates on traditional methods for combating malaria and emerging Wolbachia-based interventions

The escalating challenge of malaria control necessitates innovative approaches that extend beyond traditional control strategies. This review explores the incorporation of traditional vector control techniques with emerging Wolbachia-based interventions. Wolbachia, a naturally occurring bacteria, offers a novel approach for combatting vector-borne diseases, including malaria, by reducing the mosquitoes' ability to transmit these diseases. The study explores the rationale for this integration, presenting various case studies and pilot projects that have exhibited significant success. Employing a multi-dimensional approach that includes community mobilization, environmental modifications, and new biological methods, the paper posits that integrated efforts could mark a turning point in the struggle against malaria. Our findings indicate that incorporating Wolbachia-based strategies into existing vector management programs not only is feasible but also heightens the efficacy of malaria control initiatives in different countries especially in Pakistan. The paper concludes that continued research and international collaboration are imperative for translating these promising methods from the laboratory to the field, thereby offering a more sustainable and effective malaria control strategy.

Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data

OBJECTIVE

This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission.

METHOD

Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed.

RESULTS

From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation.

CONCLUSION

In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue's complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.

Aedes aegypti Controls AE. Aegypti: SIT and IIT-An Overview

The sterile insect technique (SIT) and the incompatible insect technique (IIT) are emerging and potentially revolutionary tools for controlling Aedes aegypti (L.), a prominent worldwide mosquito vector threat to humans that is notoriously difficult to reduce or eliminate in intervention areas using traditional integrated vector management (IVM) approaches. Here we provide an overview of the discovery, development, and application of SIT and IIT to Ae. aegypti control, and innovations and advances in technology, including transgenics, that could elevate these techniques to a worldwide sustainable solution to Ae. aegypti when combined with other IVM practices.

Ethical considerations related to drone use for environment and health research: A scoping review protocol

INTRODUCTION

The use of drones in environment and health research is a relatively new phenomenon. A principal research activity drones are used for is environmental monitoring, which can raise concerns in local communities. Existing ethical guidance for researchers is often not specific to drone technology and practices vary between research settings. Therefore, this scoping review aims to gather the evidence available on ethical considerations surrounding drone use as perceived by local communities, ethical considerations reported on by researchers implementing drone research, and published ethical guidance related to drone deployment.

METHODS AND ANALYSIS

This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) guidelines. The literature search will be conducted using academic databases and grey literature sources. After pilot testing the inclusion criteria and data extraction tool, two researchers will double-screen and then chart available evidence independently. A content analysis will be carried out to identify patterns of categories or terms used to describe ethical considerations related to drone usage for environmental monitoring in the literature using the R Package RQDA. Discrepancies in any phase of the project will be solved through consensus between the two reviewers. If consensus cannot be reached, a third arbitrator will be consulted.

ETHICS AND DISSEMINATION

Ethical approval is not required; only secondary data will be used. This protocol is registered on the Open Science Framework (osf.io/a78et). The results will be disseminated through publication in a scientific journal and will be used to inform drone field campaigns in the Wellcome Trust funded HARMONIZE project. HARMONIZE aims to develop cost-effective and reproducible digital infrastructure for stakeholders in climate change hotspots in Latin America & the Caribbean and will use drone technology to collect data on fine scale landscape changes.

Challenge and opportunity for vector control strategies on key mosquito-borne diseases during the COVID-19 pandemic

Mosquito-borne diseases are major global health problems that threaten nearly half of the world's population. Conflicting resources and infrastructure required by the coronavirus disease 2019 (COVID-19) global pandemic have resulted in the vector control process being more demanding than ever. Although novel vector control paradigms may have been more applicable and efficacious in these challenging settings, there were virtually no reports of novel strategies being developed or implemented during COVID-19 pandemic. Evidence shows that the COVID-19 pandemic has dramatically impacted the implementation of conventional mosquito vector measures. Varying degrees of disruptions in malaria control and insecticide-treated nets (ITNs) and indoor residual spray (IRS) distributions worldwide from 2020 to 2021 were reported. Control measures such as mosquito net distribution and community education were significantly reduced in sub-Saharan countries. The COVID-19 pandemic has provided an opportunity for innovative vector control technologies currently being developed. Releasing sterile or lethal gene-carrying male mosquitoes and novel biopesticides may have advantages that are not matched by traditional vector measures in the current context. Here, we review the effects of COVID-19 pandemic on current vector control measures from 2020 to 2021 and discuss the future direction of vector control, taking into account probable evolving conditions of the COVID-19 pandemic.

Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.